Computational design and engineering of self-assembling multivalent microproteins with therapeutic potential against SARS-CoV-2

Multivalent drugs targeting homo-oligomeric viral surface proteins, such as the SARS-CoV-2 trimeric spike (S) protein, have the potential to elicit more potent and broad-spectrum therapeutic responses than monovalent drugs by synergistically engaging multiple binding sites on viral targets. However, rational design and engineering of nanoscale multivalent protein drugs are still lacking. Here, we developed a computational approach to engineer self-assembling trivalent microproteins that simultaneously bind to the three receptor binding domains (RBDs) of the S protein. This approach involves four steps: structure-guided linker design, molecular simulation evaluation of self-assembly, experimental validation of self-assembly state, and functional testing. Using this approach, we first designed trivalent constructs of the microprotein miniACE2 (MP) with different trimerization scaffolds and linkers, and found that one of the constructs (MP-5ff) showed high trimerization efficiency, good conformational homogeneity, and strong antiviral neutralizing activity. With its trimerization unit (5ff), we then engineered a trivalent nanobody (Tr67) that exhibited potent and broad neutralizing activity against the dominant Omicron variants, including XBB.1 and XBB.1.5. Cryo-EM complex structure confirmed that Tr67 stably binds to all three RBDs of the Omicron S protein in a synergistic form, locking them in the “3-RBD-up” conformation that could block human receptor (ACE2) binding and potentially facilitate immune clearance. Therefore, our approach provides an effective strategy for engineering potent protein drugs against SARS-CoV-2 and other deadly coronaviruses. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s12951-024-02329-3.

Table S1.The amino acid sequences of the trivalent constructs.Table S2.MM/GBSA binding free energies of trivalent constructs in MD simulations.Table S3.Cryo-EM data collection and refinement statistics.Table S4.Interfacial residues of monovalent Nb67 and Tr67 binding to RBD.Table S5.Interfacial residues of the RBDs of the cluster 1 variants with the monovalent Nb67.Table S6.Binding interface areas and numbers of interfacial residues of the best-scoring docking poses of Nb67 and Tr67 to the RBDs of the tested Omicron variants.449,455,456,475,483,484,485,486,487,488,489,490,493,494 BA.2 449,455,456,475,484,485,486,487,488,489,490,492,493,494 BA.2.75 449,483,484,485,486,489,490,492,493,494 BA.2.12.1 449,452,455,456,475,484,485,486,487,488,489,490,492,493,494 BA.3 449,455,475,483,484,485,486,487,488,489,490,493,494 b The mutation at 486 was found to be the key residue for the binding to cluster 1 variants, distinguishing them from cluster 2 variants BA.5, BF.7, BQ.1.1,XBB.1, and XBB.1.5(as shown in Fig. S5).The interface areas and residue numbers of both the Nb67-RBD and Tr67-RBD complexes were calculated based on the binding interactions with a single RBD.

Figure S2 .
Figure S2.Principal component analysis (PCA) for a MD trajectory of MP-5ff.

Figure S3 .
Figure S3.The free energy landscapes (FELs) of the MD conformations of Tr67.

Figure S5 .
Figure S5.RBD mutations in the Omicron variants tested.

Figure S6 .
Figure S6.Best-scoring PyDock docking poses of monovalent Nb67 to the RBDs of the Omicron variants.

Figure S7 .
Figure S7.Best-scoring PyDock docking poses of Tr67 to the RBDs of the Omicron variants.

Figure S1 .
Figure S1.Time-dependent RMSDs of the trivalent constructs averaged over three independent MD trajectories, with their initial structures as the references.(a) The RMSD results of the four F-scaffold constructs.(b) The RMSD results of the four C-scaffold constructs.

Figure S2 .
Figure S2.Principal component analysis (PCA) for a MD trajectory of the trivalent construct MP-5ff.(a) Projection of the trajectory onto the first and the second principal components (PC1 and PC2).(b) Projection of the trajectory onto the second and the third principal components (PC2 and PC3).(c) Projection of the trajectory onto the first and the third principal components (PC1 and PC3).(d) Corresponding eigenvalue contributions of the principal components to the variance of the data.

Figure S3 .
Figure S3.The free energy landscapes (FELs) of the MD conformations of the trivalent nanobody Tr67.(a) FEL for the simulated conformational projections onto the first and the second principal components (PC1 and PC2), indicating that there exists only a deep free-energy well (in blue).(b) FEL for the simulated conformational projections onto two alternative reaction coordinates: root mean square deviation (RMSD) and radius of gyration (Rg), showing again that there exists only a deep free-energy well (in blue).Both FELs showed that Tr67 has only one low-energy trimer conformation, suggesting a good conformational homogeneity similar to that of MP-5ff.

Figure S4 .
Figure S4.Flowchart for the single-particle cyro-EM analysis of Tr67 in complex with Omicron BA.1 spike protein.The overall resolution of the EM density map is ~9 Å with the Fourier shell correlation (FSC) at 0.143.

Figure S5 .
Figure S5.RBD mutations in the Omicron variants tested.Blank positions indicate residues conserved relative to BA.1, and colored positions highlight residues with mutations different from the BA.1 sequence.Structural models of the RBDs of the variants were built using the BA.1 atomic model as the template and the RosettaRomodel program 62 .A total of 500 low-energy models were generated for each variant, and the lowest-energy model was selected as the input structure for molecular docking.

Figure S6 .
Figure S6.Best-scoring PyDock docking poses of monovalent Nb67 to the RBDs of the Omicron variants.(a) Nb67 docked to the expected epitope on the upper region of the RBD for variants BA.1, BA.2, BA.2.75, BA.2.12.1, and BA.3 (cluster 1), but to the lower region for variants BA.5, BF.7, BQ.1.1,XBB.1, and XBB.1.5(cluster 2).(b)The docking Nb67-RBD complexes in the S proteins with the 1-RBD-up conformation.The Nb67s of the cluster 2 variants might collide with other parts of the S protein, suggesting that they are sterically unfavorable for effective binding to the RBDs.The PyDOCK server at https://life.bsc.es/pid/pydock was used to perform the docking simulations, and PyDOCK scoring is based on an empirical potential composed of electrostatic, desolvation, and van der Waals energy terms.

Figure S7 .
Figure S7.Best-scoring PyDock docking poses of Tr67 to the RBDs of the Omicron variants.The docking Tr67spike complexes in the superimposed S proteins with the 3-RBD-up conformation.The PyDOCK server at https://life.bsc.es/pid/pydock was used to perform the docking simulations.
Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai 200438, China 2 ACROBiosystems Inc., Beijing 100176, China 3 Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 201203, China *Corresponding author at: School of Life Sciences, Fudan University, Shanghai 200438, China.

Table S2 .
MM/GBSA binding free energies of trivalent constructs in MD simulations.

Table S3 .
Cryo-EM data collection and refinement statistics

Table S4 .
Interfacial residues of monovalent Nb67 and Tr67 binding to RBD. a a Interfacial residues were identified by a 4-Å distance cutoff between the atoms of Nb67 and those of RBD.Residues involved in hydrogen bonding are highlighted in red and those forming salt bridges are highlighted in yellow.

Table S5 .
Interfacial residues of the RBDs of the cluster 1 variants with the monovalent Nb67.b

Table S6 .
Binding interface areas and numbers of interfacial residues of the best-scoring docking poses of Nb67 and Tr67 to the RBDs of the tested Omicron variants.c